Development and Application of a Risk Assessment System for Premature Rupture of Membranes — Machine Learning Technology Based on Climate Variables and Time

Premature rupture of membranes (PROM) can disrupt the original delivery plan, leading to premature delivery and a variety of adverse complications. Special obstetric examinations currently used for prediction are expensive. In this study, a machine learning model based on time series technology was constructed using clinical features and meteorological data to explore the feasibility of the model in predicting PROM. The results show that the GRU model combined with meteorological data performs best, and more than 92 % of PROMs are correctly classified as non-low risk. The prediction model is effective and economical, which helps pregnant women prevent and improve perinatal prognosis, and provides managers with hierarchical management and public health decision support.